DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.

ABSTRACT: In silico prediction of drug-target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug-target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug-Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug-target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network b

SUBMITTER: Thafar MA 

PROVIDER: S-EPMC7325230 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature